CN113655027A - Method for rapidly detecting tannin content in plant by near infrared - Google Patents
Method for rapidly detecting tannin content in plant by near infrared Download PDFInfo
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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Abstract
The invention relates to a method for quickly detecting tannin content in plants by near infrared, which comprises the steps of firstly, measuring the component content of tannin in a plant sample by adopting an ultraviolet spectrophotometer measurement method; scanning absorption spectrum data of a plant sample by using a near infrared spectrum tester; then selecting a tannin spectrum pretreatment method; establishing a plant variety identification model by utilizing the scanned absorption spectrum data; establishing a spectral analysis model by combining the detection value of the ultraviolet spectrophotometer measurement method and the spectral data obtained by the spectral preprocessing method; selecting other samples to correct the spectral analysis model; and finally, establishing a quantitative model by using the initial predicted value obtained by the corrected spectral analysis model and the actual measured value, predicting the plant variety by using the identification model and predicting the content of tannin in the plant to be detected by using the quantitative model. The tannin content in the plant can be detected quickly and efficiently.
Description
Technical Field
The invention belongs to the technical field of traditional Chinese medicine content detection, and particularly relates to a method for rapidly detecting tannin content in plants by near infrared.
Background
Tannins (or tannins) are a class of astringent polyphenol biomolecules that bind to and precipitate proteins and various other organic compounds, including amino acids and alkaloids. Tannin compounds are widely distributed in various plants and play a protective role in the plants. At present, tannin applied at home and abroad is mainly extracted from natural plants containing tannin and plant gall gallnut by water or other solvents. The tannin determination method comprises a classical method and a modern method, wherein the classical method comprises a leather powder method, a volumetric method, a colorimetric method and a spectrophotometry method, the modern method comprises a High Performance Liquid Chromatography (HPLC), an atomic absorption spectrophotometry method, a thin layer scanning method, a thermal lens spectrometry method, a high-sensitivity oscillometric potential kinetic analysis method, an electrochemical sensor method and the like, and the methods have the main defects of complex operation, long time consumption and incapability of being carried out in a large scale.
The near infrared spectrum analysis technology has the advantages of rapidness, accuracy, no pollution and the like, so that a rapid and accurate near infrared prediction model for detecting tannin in plants is established, the time is shortened, the cost is reduced, a reference can be provided for accurately evaluating the quality of the plants, and a rapid screening technology can be provided for the quality of raw materials required by enterprise production.
Disclosure of Invention
The invention aims to provide a method for quickly detecting tannin content in a plant by near infrared so as to realize quick and efficient detection of tannin content in the plant, accurately evaluate the quality of the plant and provide a quick screening technology for the quality of raw materials required by enterprise production.
The technical scheme adopted by the invention is that the method for rapidly detecting the tannin content in the plant by near infrared comprises the following steps:
(1) measuring the component content of tannin in the plant sample by adopting an ultraviolet spectrophotometer;
(2) scanning absorption spectrum data of the sample plant by using a near infrared spectrum tester;
(3) selecting a tannin spectrum pretreatment method;
(4) establishing a plant variety distinguishing model;
(5) establishing a spectral analysis model by using the scanned absorption spectrum data and combining a detection value of an ultraviolet spectrophotometer and spectrum data obtained by a spectrum preprocessing method through a combined interval partial least squares (si-PLS);
(6) selecting and verifying plant powder to correct the spectral analysis model;
(7) and establishing a quantitative model by using the initial predicted value obtained by the corrected spectral analysis model and the actual measured value of the ultraviolet spectrophotometer, and predicting the content of tannin in the plant powder to be detected by using the quantitative model.
In the step (1), the tannin content is detected at least three times by adopting an ultraviolet spectrophotometer method.
The method for acquiring the near-infrared spectrogram in the step (2) comprises the following steps: weighing a plant sample, crushing, and sieving with a 300-mesh sieve, wherein the obtained sample powder uses an integrating sphere to collect a near-infrared spectrogram, and parameters of the near-infrared spectrometer are set as follows: the spectrum collection range is 10000-4000 cm-1Resolution of 8cm-1The average spectrum was obtained by scanning 64 times at 25 ℃ and 3 times per sample.
The method for preprocessing the tannin spectrum selected in the step (3) is a method for preprocessing raw spectrum data, which enables the decision coefficient and the resolution of a spectrum analysis model to be maximum and the correction standard deviation and the relative standard deviation to be minimum.
And (4) carrying out discriminant analysis on the plant varieties in one-to-one correspondence with the average values of the absorption spectrum data after spectrum preprocessing by utilizing TQ Analyst near infrared spectrum analysis software, and carrying out modeling by adopting the Mahalanobis distance.
And (5) performing linear fitting on the actual measurement value of the tannin content in the plant sample powder and the average value of the absorption spectrum data after spectrum pretreatment in a one-to-one correspondence manner by utilizing TQ analysis near infrared spectrum analysis software, and modeling by adopting si-PLS, wherein the absorption peak value of a characteristic spectrum area is 5200-4600 cm-1And 7600-7000 cm-1。
The si-PLS modeling is to optimize the Model by selecting the near infrared spectrum wave band through a Model function by utilizing matlab software.
And (6) correcting the spectral analysis model by minimizing the standard deviation of the distribution of the predicted values of the spectral data of the correction set.
And the tannin in the plant is subjected to spectrum pretreatment data verification by adopting multivariate signal correction, derivative, noise filtration, baseline correction and the like.
The quantitative model is established by the spectral data predicted value and the actual measured value obtained by utilizing the corrected spectral analysis model; wherein, the predicted value of the spectral data of tannin in the plant is taken as the ordinate; the actual measurement value of tannin in the plant is used as the abscissa, and the model predicts the tannin content range to be 25.00% -85.00%.
And (7) predicting the content of tannin in the plant to be tested by using a quantitative model, namely inputting the average value of absorption spectrum data of the plant powder to be tested, which is acquired by the near infrared spectrum analyzer, into the spectral analysis model, obtaining a predicted value of the spectral data through spectral data preprocessing and spectral analysis model calculation, inputting the predicted value of the spectral data into the quantitative model, and obtaining the predicted value of the content of tannin in the plant to be tested through quantitative model calculation.
The technical scheme of the invention has the following advantages:
according to the method, different varieties of plants are collected, and the discrimination models of the plant varieties and the near-infrared spectrograms in one-to-one correspondence are established by utilizing the near-infrared spectrograms, so that the plant varieties can be discriminated quickly, efficiently and accurately.
The invention relates to a method for quickly detecting tannin content in plants by near infrared, which establishes a spectral analysis model by utilizing the measured tannin content (the tannin content range is between 71.98 percent and 47.68 percent) in sample plants to realize quick, efficient and accurate detection of the tannin content in plant powder, has low cost and provides a quick screening technology for the raw material quality required by enterprise production.
The invention adopts a second derivative optimal spectrum preprocessing method for a spectrum prediction model, and the absorption peak value of a characteristic spectrum region is 5200-4600 cm-1And 7600-7000 cm-1. After verification: the tannin content spectral model cross validation Root Mean Square Error (RMSECV) was 2.55 and the predicted Root Mean Square Error (RMSEP) was 2.60. After verification: the correlation coefficient of the built tannin prediction spectrum model is 1.0000, and the method can be used for daily detection.
Description of the drawings:
FIG. 1 is a graph of the original spectra of Galla chinensis and Tara samples.
FIG. 2 is a plant species discrimination model in which "□" represents Galla chinensis and "Δ" represents Tara.
FIG. 3 is a graph relating total wavelength partial least squares (Full-PLS) tannin measurements to predicted values.
FIG. 4 is a graph showing correlation between measured tannin values and predicted tannin values by the joint interval partial least squares (Si-PLS) method.
The specific implementation mode is as follows:
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1 Galla chinensis and Tara content detection method
(1) Measuring the component content of tannin in gallnut and tara samples by adopting an ultraviolet spectrophotometer for at least three times;
(2) weighing Galla chinensis and Tara sample, pulverizing, sieving with 300 mesh sieve, collecting the obtained sample powder with integrating sphereCollecting a near-infrared spectrogram, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000 cm-1Resolution of 8cm-1Scanning for 64 times at 25 ℃, collecting each sample for 3 times, and calculating an average spectrum;
(3) and selecting a raw spectral data preprocessing method which maximizes the decision coefficient and resolution of the spectral analysis model and minimizes the corrected standard deviation and the relative standard deviation. The gallnut and tara powder are subjected to spectrum preprocessing data verification by adopting multivariate signal correction, derivative, noise filtering, baseline correction and the like.
(4) And (3) carrying out discriminant analysis on the sample varieties in one-to-one correspondence with the average value of the absorption spectrum data after spectrum preprocessing by utilizing TQ Analyst near infrared spectrum analysis software, modeling by adopting the Mahalanobis distance, and selecting the object to be detected.
(5) And establishing a near infrared spectrum analysis model by using the average value of the measured value of the content of the tannin and the average value of the absorption spectrum data of multiple scans. And performing linear fitting on the actual measurement value of the tannin content in the gallnut and tara sample powder and the average value of the absorption spectrum data after spectrum pretreatment in a one-to-one correspondence manner by utilizing TQ Analyst near infrared spectrum analysis software, and modeling by adopting si-PLS. And (3) selecting the waveband of the near infrared spectrum by using matlab software through a Model function so as to optimize the Model, wherein the absorption peak value of a characteristic spectrum region is 5200-4600 cm-1And 7600-7000 cm-1. Establishing a quantitative model by using the spectral data predicted value and the actual measured value obtained by the corrected spectral analysis model; wherein, the predicted value of the spectral data of tannin in the sample is taken as the ordinate; and taking the actual measured value of the tannin in the sample as an abscissa, and predicting the tannin content range to be 25.00-85.00% by using a model.
(6) Selecting verification sample powder to correct the spectral analysis model;
(7) the method comprises the steps of predicting the content of tannin in a sample to be detected by using a quantitative model, inputting the average value of absorption spectrum data of sample powder to be detected, collected by a near infrared spectrum measuring instrument, into a spectrum analysis model, preprocessing the spectrum data, calculating the spectrum analysis model to obtain a predicted value of the spectrum data, inputting the predicted value of the spectrum data into the quantitative model, and calculating the predicted value of the content of tannin in the sample to be detected by using the quantitative model.
To further verify the effectiveness of the present invention, the inventors performed a series of tests, as follows:
firstly, a test material: the test materials were 37 parts of gallnut and tara samples, each divided into 2 groups, one group for chemical composition determination and the other group for near infrared spectrum acquisition. The near infrared spectrum sample set is divided into a correction set and an inspection set, the correction set is used for establishing a model and performing internal cross validation on the model, and the inspection set is used for performing external validation on the model. 27 parts were selected as the calibration set and 10 parts were selected as the test set for this experiment.
Secondly, collecting a near-infrared scanning spectrum:
weighing a sample, crushing, sieving with a 300-mesh sieve, collecting a near-infrared spectrogram of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000 cm-1Resolution of 8cm-1The average spectrum was obtained by scanning 64 times at 25 ℃ and 3 times per sample, as shown in FIG. 1.
Determination of content of tannin component
The statistical results of the near infrared model calibration set and the test set of the tannin component are shown in table 1. The tannin content ranges from 71.98% to 47.68%, the average value is 61.78%, the distribution range of the chemical component content of the sample is obviously different, and the sample has certain representativeness.
TABLE 1 determination of the content of vegetable tannin components
| Number of samples | Content range | Mean value of | Standard deviation of | |
| Tannin (%) | 37 | 71.98-47.68 | 61.78 | 7.98 |
Four, collection of original spectrum
FIG. 1 shows that the concentration of Galla chinensis and Tara sample is 10000-4000 cm-1The near infrared diffuse reflection spectrogram has a plurality of absorption peaks in the whole wavelength range. The absorption spectrum characteristics of different samples in the near infrared spectrum region are basically consistent, the full spectrum shows a trend of flat height, which indicates that the main components of the samples are basically the same, but the relative contents of all the components are different, and the near infrared spectrum of the samples can be used for measuring the tannin content.
Fifthly, preprocessing an original spectrum and establishing a model:
the original spectrum preprocessing method comprises six methods of centralization, range normalization, vector correction, scattering correction, first derivative and second derivative. The selection of the original spectrum preprocessing method directly influences the establishment of an analysis model. And modeling by adopting a Quantitative Partial Least Squares (QPLS) method, wherein the larger the decision coefficient (R2) and the Resolution (RPD) of the constructed near-infrared prediction model are, the smaller the corrected standard deviation (SEC) and the Relative Standard Deviation (RSD) are, and the more accurate the prediction model is. Through the above six pretreatment methods and principal component number method treatment comparative analysis, the results are shown in table 2, and it is found that: when MSC + FD + Ns combination is adopted, RMSEC is minimum, Rc2At maximum, this is the optimal spectral pre-processing method.
TABLE 2 phytochemical composition tannin pretreatment results
MSC, correcting the multi-element signals; FD/SD is the first/second derivative; ns, SG, Nd: filtering (smoothing) method
Sixthly, establishing a qualitative model:
and the TQ Analyst near infrared analysis software is used for carrying out mathematical processing on the absorption spectrum data, so that the discrimination model is more accurate. And (3) dividing the sample into a training set and a prediction set by adopting Mahalanobis distance discriminant analysis, judging the classification effect by the prediction accuracy of the prediction set, and performing grade prediction on the spectrum by using the established grade judgment model, as shown in figure 2.
In the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
In the formula of1And mu2Mean matrix of global G1 and G2; s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
establishment of a Full-PLS quantitative model:
the method comprises the steps of performing mathematical processing on absorption spectrum data by utilizing TQ Analyst near-infrared calibration software, enabling a linear relation corresponding to a chemical measurement value and a corresponding absorption spectrum data predicted value to be more accurate, namely predicting a spectrum analysis model to be more accurate, obtaining different results for 27 samples by using different mathematical algorithms and different mathematical conversions, then associating the chemical measurement value of tannin in 27 sample sets of a correction set with a primary predicted value of the spectrum analysis model, making x (measurement value) for the spectrum data predicted value of the correction set, making y (predicted value) for the corresponding chemical measurement value, respectively making 27 data values (x, y), and preprocessing the spectrum data according to a screened spectrum preprocessing method. As shown in fig. 3, the corrected predicted value and the chemically measured value have a good linear relationship. The correlation coefficient for the tannin content determination was 0.9851. Obtaining a prediction model of the tannin content in the plant; wherein, the predicted value of the spectral data of tannin in the plant is taken as the ordinate; the chemical actual measurement of tannins in plants is taken as the abscissa.
Seventhly, correcting the model:
10 samples which do not participate in calibration are selected to correct the prediction model of the established tannin. The verification results are shown in table 3 below. The absolute error between the predicted value and the chemical value of the tannin is less than 1, and the error is small, so that the feasibility of the plant tannin content prediction model is high, and the prediction result is accurate.
TABLE 3 near Infrared model verification results
Preferably, the establishment of a Si-PLS quantitative model:
the method comprises the steps of performing mathematical processing on absorption spectrum data by utilizing TQ Analyst near-infrared calibration software, enabling a linear relation corresponding to a chemical measurement value and a corresponding absorption spectrum data predicted value to be more accurate, namely predicting a spectrum analysis model to be more accurate, obtaining different results for 27 samples by using different mathematical algorithms and different mathematical conversions, then associating the chemical measurement value of tannin in 27 sample sets of a correction set with a primary predicted value of the spectrum analysis model, making x (measurement value) for the spectrum data predicted value of the correction set, making y (predicted value) for the corresponding chemical measurement value, respectively making 27 data values (x, y), and preprocessing the spectrum data according to a screened spectrum preprocessing method. As shown in fig. 4, the corrected predicted value and the chemically measured value have a good linear relationship. The correlation coefficient of the tannin content determination was 1.0000. Obtaining a prediction model of the tannin content in the plant; wherein, the predicted value of the spectral data of tannin in the plant is taken as the ordinate; the chemical actual measurement of tannins in plants is taken as the abscissa.
And (3) correcting the model:
10 samples which do not participate in calibration are selected to correct the prediction model of the established tannin. The verification results are shown in table 4 below. The absolute error between the predicted value and the chemical value of the tannin is less than 0.5, and the error is small, so that the feasibility of the plant tannin content prediction model is high, and the prediction result is accurate.
TABLE 4 near-Infrared model verification results
The prediction model is modeled by using an optical data prediction value and a value actually measured by a chemical method, and tannin indexes detected by the chemical method are performed according to national standards, so that time and labor are wasted; the early-stage modeling needs to obtain chemical values of a certain number of plants, after the models are built, the models need to be opened in near-infrared analysis software, and the prediction models are built by using real measured values, so that the reliability of predicted values is guaranteed. In practical application, the plant sample is scanned by only adopting a near-infrared spectrometer to obtain optical data, the content of tannin in the plant is measured by directly utilizing a model without adopting a chemical method for measurement, and the method is simple, quick, efficient, money-saving and labor-saving.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A method for rapidly detecting the content of tannin in plants by near infrared is characterized by comprising the following steps:
(1) measuring the component content of tannin in the plant sample by adopting an ultraviolet spectrophotometer;
(2) scanning absorption spectrum data of the sample plant by using a near infrared spectrum tester;
(3) selecting a tannin spectrum pretreatment method;
(4) establishing a plant variety distinguishing model;
(5) establishing a spectral analysis model by combining the scanned absorption spectrum data, the detection value of an ultraviolet spectrophotometer and the spectrum data obtained by a spectrum preprocessing method and by a combined interval partial least square method si-PLS;
(6) selecting and verifying plant powder to correct the spectral analysis model;
(7) and establishing a quantitative model by using the initial predicted value obtained by the corrected spectral analysis model and the actual measured value of the ultraviolet spectrophotometer, and predicting the content of tannin in the plant powder to be detected by using the quantitative model.
2. The method of claim 1, wherein the method of step (1): measuring the tannin content by using an ultraviolet spectrophotometer at least three times; in the step (2), the scanning of the absorption spectrum of the plant powder is repeated at least three times.
3. The method according to claim 1, wherein the step (2) of collecting the near-infrared spectrogram is as follows: weighing a plant sample, crushing, and sieving with a 300-mesh sieve, wherein the obtained sample powder uses an integrating sphere to collect a near-infrared spectrogram, and parameters of the near-infrared spectrometer are set as follows: spectrum collection range 10000-4000cm-1Resolution of 8cm-1The average spectrum was obtained by scanning 64 times at 25 ℃ and 3 times per sample.
4. The method of claim 1, wherein the step (3) of selecting the tannin spectral preprocessing method is selecting the raw spectral data preprocessing method that maximizes the resolution and the coefficient of determination of the spectral analysis model and minimizes the corrected standard deviation and the relative standard deviation.
5. The method as claimed in claim 1, wherein the step (4) establishes a plant variety discrimination model, discriminates and analyzes the plant variety and the average value of the absorption spectrum data after the spectrum preprocessing in a one-to-one correspondence manner by utilizing TQ analysis near infrared spectrum analysis software, and adopts Mahalanobis distance for modeling to select the object to be tested.
6. The method according to claim 1, wherein the step (5) is to establish a near infrared spectrum analysis model using an average value of measured values of the content of tannin and an average value of absorption spectrum data of a plurality of scans;
the process of establishing the spectral analysis model in the step (5) is as follows: utilizing TQ Analyst near infrared spectrum analysis software to perform linear fitting on the actual measured value of the tannin content in the plant powder sample and the average value of the absorption spectrum data after spectrum pretreatment in a one-to-one correspondence manner, and modeling by adopting si-PLS;
the process of step (5) si-PLS is as follows: and (3) selecting the waveband of the near infrared spectrum by using matlab software through a Model function so as to optimize the Model, wherein the absorption peak value of a characteristic spectrum region is 5200-4600 cm-1And 7600-7000 cm-1。
7. The method for near-infrared rapid detection of tannin content in plant powder as claimed in claim 1, wherein said step (6) of correcting the spectral analysis model is to minimize the standard deviation of the distribution of predicted values of spectral data in the correction set.
8. The method for near-infrared rapid detection of tannin content in plant powder as claimed in claim 4, wherein the plant powder tannin is subjected to spectrum pretreatment data verification by using multivariate signal correction, derivative, noise filtering and baseline correction.
9. The method for near-infrared rapid detection of tannin content in plant powder as claimed in claim 6, wherein said spectral data prediction value obtained by using said corrected spectral analysis model and actual measurement value are used to build quantitative model; wherein, the predicted value of the spectral data of tannin in the plant is taken as the ordinate; the actual measurement value of tannin in the plant is used as the abscissa, and the model predicts the tannin content range to be 25.00% -85.00%.
10. The method as claimed in claim 1, wherein the step (7) of predicting the tannin content in the plant to be tested by using the quantitative model comprises the steps of inputting the average value of the absorption spectrum data of the plant powder to be tested, collected by the near infrared spectrometer, into the spectral analysis model, obtaining a predicted value of the spectral data through spectral data preprocessing and spectral analysis model calculation, inputting the predicted value of the spectral data into the quantitative model, and obtaining the predicted value of the tannin content in the plant to be tested through quantitative model calculation.
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